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A Review on Smart Healthcare employing Quantum Internet of Things

IEEE engineering Management Review 2024年 1-9页

作者： Sutradhar, Kartick Venkatesh, Ranjitha Venkatesh, Priyanka Department of Computer Science and Engineering Indian Institute of Information Technology Sri City India Department of Computer Science and Engineering GITAM School of Technology Bengaluru India Department of Computer Science and Engineering Presidency University Bengaluru India

The Quantum Internet of Things (QIoT) in the healthcare industry holds the promise of transforming patient care, diagnostics, and medical research. Quantum-enhanced sensors, communication, and computation offer unprecedented capabilities that can revolutionize how healthcare services are delivered and experienced. This paper explores the potential of QIoT in the context of smart healthcare, where interconnected quantum-enabled devices and systems create an ecosystem that enhances data security, enables real-time monitoring, and advances medical knowledge. We delve into the applications of quantum sensors in precise health monitoring, the role of quantum communication in secure telemedicine, and the computational power of quantum computing in drug discovery and personalized medicine. We discuss challenges such as technical feasibility, scalability, and regulatory considerations, along with the emerging trends and opportunities in this transformative field. By examining the intersection of quantum technologies and smart healthcare, this paper aims to shed light on the novel approaches and breakthroughs that could redefine the future of healthcare delivery and patient outcomes. IEEE

关键词： Internet of things

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Strong spin frustration and magnetism in kagoméantiferromagnets LnCu_(3)(OH)_(6)Br_(3)(Ln=Nd,Sm,and Eu)

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Chinese Physics B 2023年第4期32卷 103-108页

作者：钟金群余振伟岳小宇王义炎梁慧孙燕吴丹丹丁宗玲孙进孙学峰李秋菊 School of Materials Science and Engineering Anhui UniversityHefei 230601China Institute of Physical Science and Information Technology Anhui UniversityHefei 230601China Department of Physics and Key Laboratory of Strongly-Coupled Quantum Matter Physics(CAS)University of Science and Technology of ChinaHefei 230026China School of Physics and Optoelectronic engineering Anhui UniversityHefei 230601China School of Optical and Electronic Information Suzhou City UniversitySuzhou 215104China

To study the effects of lanthanide ions on the geometrically frustrated antiferromagnets and their magnetic properties,we grew high-quality single crystals of LnCu_(3)(OH)_(6)Br_(3)(Ln=Nd,Sm,and Eu)by hydrothermal method and studied their crystal structures and magnetic *** refinements of the crystal structure referred to the powder x-ray diffraction data show that LnCu_(3)(OH)_(6)Br_(3)adopt a Kapellasite-type layer structure,which is isostructural to their chlorine *** susceptibilities demonstrate that LnCu_(3)(OH)_(6)Br_(3)have strong antiferromagnetic coupling and a pronounced magnetic frustration *** measurements indicate canted antiferromagnetic ordering of Cu^(2+)ions around 16 K within the kagoméplane and weak ferromagnetic ***,shoulder-like anomalies in specific heat around 16 K could be a signature of emergent of magnetic *** low-temperature negative magnetization and specific heat of LnCu_(3)(OH)_(6)Br_(3)(Ln=Nd,Sm,and Eu)indicate that Ln^(3+)ions induce more exotic magnetic ground state properties.

关键词： kagomélattice antiferromagnet single crystal spin frustration

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High-Magnetic-Field-Induced First-Order Magnetic Transition and Tricritical Phenomenon in Antiferromagnetic Semimetal NdSb

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Chinese Physics Letters 2025年第2期42卷 125-131页

作者： Jun Zhao Azizur Rahman Wei Liu Langsheng Ling Yuyan Han Chuanying Xi Li Pi Zhe Qu Hao Gao Lei Zhang School of Mechanical and Electrical Engineering Sanming UniversitySanming 365004China Anhui Key Laboratory of Low-Energy Quantum Materials and Devices High Magnetic Field LaboratoryHefei Institutes of Physical ScienceChinese Academy of SciencesHefei 230031China Department of Physics Lab of Low-dimensional Magnetism and Spintronic DevicesSchool of PhysicsHefei University of TechnologyHefei 230009China Institutes of Physical Science and Information Technology Anhui UniversityHefei 230601China Hefei National Research Center for Physical Sciences at Microscale University of Science and Technology of ChinaHefei 230026China

The antiferromagnetic(AFM)semimetal NdSb is well known for the interplay between its exotic magnetism and topological ***,its magnetism remains poorly *** this study,we thoroughly investigated the magnetization of NdSb single crystals with a high magnetic field(H)of up to 30T applied in various *** found that the AFM phase is suppressed by a magnetic field of 9.41T when H‖[100]and 11.25T when H‖[110],whereas the suppression field ranges from 9.41 to 10.67T with a hysteresis of 1.26T when H‖[111].The magnetization of H‖[100],which is an easy direction with a typical magnetic transition,was studied in *** AFM phase with H‖[100]was suppressed at lower temperatures,disappearing at approximately 6.25 *** critical exponents β=0.234(3),γ=0.824(6),and δ=4.90(6)were obtained for H‖[100],and aligned with a tricritical mean-field *** of the critical behavior suggests a fieldinduced tricritical phenomenon for H‖[100].An H-T phase diagram for an NdSb single crystal was constructed for H‖[100],revealing a field-induced first-order transition and a tricritical point(TCP)at T_(tr)=6.25K and H_(tr)=9.41 *** clarification of the multiple magnetic phases and transitions in NdSb provides crucial insights into the correlation between its magnetism and topology.

关键词： critical magnetization Transition

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A Contrastive Learning Framework for Keyphrase Extraction

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Data Intelligence 2024年第4期6卷 1032-1056页

作者： Jing Song Xian Zu Fei Xie Department of Electronic Information and Electrical Engineering Hefei Normal UniversityLianhua RoadHefei 230601China Department of Computer and Artificial Intelligence Hefei Normal UniversityLianhua RoadHefei 230601China Department of Safety Science and Engineering University of Science and Technology of ChinaHefei 230026China

Keyphrase extraction aims to extract important phrases that reflect the main topics of a document. Recently, deep learning methods are used to model semantic information and rank candidates based on the similarities between the n-grams and the document. However, existing keyphrase extraction methods mainly caused the keyphrase extraction task to be independent of the embedding. Based on the fact that phrases that are semantically closer to the document are more likely to become keyphrases, we propose a novel contrastive learning strategy for supervised keyphrase extraction by integrating local and global information of the document. A pre-trained RoBERTa model is used to model contextual information of sub-words in the document. Then, the embedding vectors of n-grams and the document are calculated by the convolution neural layers. Finally, we propose a novel loss function for efficiently ranking candidate phrases by combining n-gram features and document embeddings during the training of the model.

关键词： Keyphrase extraction Contrastive learning Supervised n-gram features Document embedding

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Joint Pilot Design and Beamforming Optimization in Massive MIMO Surveillance Systems

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China Communications 2022年第4期19卷 83-97页

作者： Caihong Kai Xiangru Zhang Xinyue Hu Wei Huang School of Computer Science and Information Engineering Hefei University of TechnologyHefei 230601China Intelligent Interconnected Systems Laboratory of Anhui Province Hefei University of TechnologyHefei 230601China School of Electronics and Information Engineering Anhui UniversityHefei 230601China

This paper proposes a novel joint channel estimation and beamforming scheme for the massive multiple-input-multiple-output(MIMO)frequency-division duplexing(FDD) wireless legitimate surveillance system. With the proposed scheme,the monitor with the full duplex capability realizes the proactive eavesdropping of the suspicious link by leveraging the pilot attack approach. Specifically, exploiting the effective eavesdropping rate and the mean square error as performance metrics and setting a total power budget at the training and transmission phases,while guaranteeing the information from suspicious source can be successfully decode, joint pilot design,power allocation and beamforming strategy are formulated as optimization problems for the two objective functions: MSE and effective eavesdropping rate. A closed-form expression of the optimal pilot with the limited length can be obtained via the channel correlation. The optimal power problem at the training phase can be solved by a simple bisection method. Then,based on the obtained imperfect estimated channel,the jamming beamforming at monitor optimization algorithm is proposed by utilizing the convex Semidefinite Programming approach to maximize the effective eavesdropping rate. Numerical results show that the proposed joint pilot design, power allocation and beamforming optimization scheme can improve the surveillance performance of the legitimate monitor as compared to the existing passive eavesdropping and jamming-assisted eavesdropping.

关键词： proactive eavesdropping massive MIMO channel estimation pilot contamination beamforming

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Efficient Local Causal Structure Learning with Privacy Preservation 11th

Efficient Local Causal Structure Learning with Privacy Pres...

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11th International Joint Conference on Rough Sets, IJCRS 2025

作者： Rong, Chen Cao, Dayuan Yu, Kui School of Computer Science and Information Engineering Hefei University of Technology Hefei230601 China

ISBN: (纸本)9783031927430

Given a target variable of a dataset, existing Federated Local Causal Structure Learning (FedLCS) approaches aim to learn direct causes and direct effects of a given target variable from decentralized data across multiple clients while considering data privacy. Since a current FedLCS approach implements iteratively conditional independence (CI) tests for skeleton construction and edge orientation from decentralized data, its computational efficiency remains a limitation. To address this limitation, in this paper, we propose an efficient FedLCS algorithm called eFedLCS that integrates GPU-accelerated computation into CI tests to speed up federated skeleton learning and skeleton orientation. Specifically, we design GPU-based G2 tests and Fisher-Z tests to reduce computational costs of CI tests. In addition, we design a caching mechanism on each client to store previously executed CI tests for minimizing redundant computations for further improving the efficiency of eFedLCS. Based on the accelerated tests, we propose a novel strategy to efficiently identify correct separation sets for skeleton orientation. Experiments with benchmark datasets have shown that eFedLCS is at least 3.6 times faster than FedLCS without significant degradation in structure learning performance. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Federated learning

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QoI-Aware Configuration Adaptation and Heterogeneous Resource Allocation for Edge Video Analytics

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IEEE Internet of Things Journal 2025年第12期12卷 19215-19230页

作者： He, Zixuan Sun, Yanjing Zhang, Beibei Dong, Kaiwen Wang, Bowen Xu, Hongli China University of Mining and Technology School of Information and Control Engineering Xuzhou221116 China China University of Mining and Technology IoT Perception Mine Research Center Xuzhou221116 China School of Information and Control Engineering China University of Science and Technology of China School of Computer Science and Technology Anhui Hefei230027 China University of Science and Technology of China Suzhou Institute for Advanced Research Jiangsu Suzhou215123 China

Edge video analytics enables agile responses of machine-centric applications by streaming videos from end devices to edge servers (ESs) for resource-intensive Deep Neural Network (DNN) inference. Quality of Inference (QoI), reflected by end-to-end analytics delay and inference accuracy, is crucial for the high-quality real-time decision-making. Due to the limited ES resources and dynamic network conditions, video configuration adaptation and fine-grained resource allocation are necessary to enhance the QoI of video streams, which involves a delicate balance between delay and accuracy. In this paper, we propose an edge-enabled multi-video analytics framework, in which performing DNN inference relies on heterogeneous resources, including CPU, GPU, and memory. Considering the different impacts of heterogeneous resources on QoI and double-queue backlogs, we formulate the joint video configuration adaptation, CPU-GPU resource allocation, and batch size selection (JCCGB) problem based on semi-Markov Decision Process to maximize the long-term average QoI. Then, Lyapunov optimization is applied to transform the original problem into one that minimizes the Lyapunov drift-plus-penalty upper bound, thereby ensuring the stability of both the transmission and computation queues. To tackle the potential multimodality of the optimal configuration adaptation and resource allocation policy, we propose the diffusion deep reinforcement learning based JCCGB (DD-JCCGB) scheme. This approach effectively enhances the policy performance and accelerates the training process. Experiments driven by real-world network traces demonstrate that the DD-JCCGB algorithm improves the long-term average QoI and outperforms baseline schemes. © 2025 IEEE.

关键词： Markov processes

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A Novel Spatio-Temporal information Knowledge Distillation Strategy for Weak-Supervised Video Anomaly Detection 7

A Novel Spatio-Temporal Information Knowledge Distillation S...

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7th International Conference on Pattern Recognition and Artificial Intelligence, PRAI 2024

作者： Cheng, Bo Zhu, Wenjia Sun, Xu Zhang, You Yu, Ye Lu, Qiang School of Computer Science and Information Engineering Hefei University of Technology Hefei China Anhui Baicheng-Huitong Technology Co. Ltd Hefei China

ISBN: (纸本)9798350350890

Video anomaly detection (VAD) is highly valuable for applications in intelligent monitoring systems. However, due to the complexity of video, as well as the lack of detailed annotation information, VAD remains a challenging problem. Although existing self-training-based methods make process in anomaly localization and reduce the need for annotation information, they still suffer from two limitations: 1) insufficient spatio-temporal information correlation in two-stage model;2) ignoring label noise. To address the above issues, we propose a novel self-training video anomaly detection framework based on spatio-temporal information knowledge distillation (STKD-SVAD). Specifically, we design a spatio-temporal information knowledge distillation strategy to establish correlations for spatial and temporal information. Furthermore, we design a temporal information module (TIM) and a spatial information module (SIM) to enhance the temporal and spatial information, respectively. Moreover, we add multi-instance loss in the second-stage of self-training framework to reduce the interference of label noise. Experimental results on public datasets demonstrate the effectiveness of our method. © 2024 IEEE.

关键词： Self-supervised learning

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Fresnel Zones Modeling and Analysis of Motion Detection for Multi-Antenna ISAC Systems 16

Fresnel Zones Modeling and Analysis of Motion Detection for ...

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16th International Conference on Wireless Communications and Signal Processing, WCSP 2024

作者： Huang, Wei Li, Yunfei Kai, Caihong He, Shiwen Li, Meng School of Computing Science and Information Engineering Hefei University of Technology Hefei230601 China School of Computer Science Central South University Changsha410083 China

ISBN: (纸本)9798350390643

Channel state information (CSI) based sensing approaches have unique advantages for motion detection. However, due to the introduction of additional angle information in multi-antenna system, the mapping relationship between channel gain and motion state is changed in the integrated sensing and communication (ISAC) scenario, which will affect the sensing performance. In this paper, we first characterize a multi-antenna Fresnel zones model that establishes a mapping relationship between the target motion parameters and channel gain. Then, we reveal that the motion state of sensing target is affected by both the path length and angle of arrival (AoA). Moreover, the analytic expressions of variation of channel amplitude related to path length and AoA are obtained. Finally, numerical results corroborate the theoretical analysis and hardware system shows the proposed sensing model can effectively detect the motion state of sensing target. © 2024 IEEE.

关键词： Mapping

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FLAG: frequency-based local and global network for face forgery detection

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Multimedia Tools and Applications 2025年第2期84卷 647-663页

作者： Zhou, Kai Sun, Guanglu Wang, Jun Wang, Jiahui Yu, Linsen School of Computer Science and Technology Harbin University of Science and Technology Harbin150080 China Department of Information Engineering and Mathematics University of Siena Siena53100 Italy

Deepfake detection aims to mitigate the threat of manipulated content by identifying and exposing forgeries. However, previous methods primarily tend to perform poorly when confronted with cross-dataset scenarios. To address the above issue, we propose an innovative hybrid network called the Frequency-based Local and Global (FLAG) network to explore local and global information with the help of frequency-domain cues for better generalization capability. In consideration of the fact that forged faces often exhibit flaws in the frequency domain, we design a Frequency-based Attention Enhancement Module (FAEM) to enhance the aggregation of CNN and Vision Transformer (ViT). In this design, local features from CNN are attentively enhanced by selected frequency coefficients in FAEM, facilitating generalizable global features learning by the ViT module. The effectiveness of the proposed method is validated via numerous experiments and the generalization performance is improved under cross-dataset scenarios. Especially, the proposed method have obtained an AUC of 99.26% and an ACC of 96.56% using intra-dataset experimental results on FaceForensics++ (C23). © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Face recognition

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